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研究生(外文):Po-Wei Cheng
論文名稱(外文):Applying Rotation Gradient and Particle Filter Techniques to Real-Time Human Detection and Tracking
外文關鍵詞:Human TrackingParticle FilterColor HistogramHOG
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With the advent of new technology and the innovation, human detection and tracking have become popular research topics. The scope of applications covers the security and surveillance, intelligent transportation systems, and home care systems. However, due to the complexity and changing background, people’s scale size, and occlusion problems, there are still limited practical applications. In order to improve the effect of detection and tracking, this study proposes a histogram oriented gradient method combined with particle filter to achieve real-time tracking of the human body. Detecting methods can be generally divided into three steps, namely, background construction, foreground subtracting and background updating. In reality, the design of those three stages is more complex in the dynamic environment. Therefore, we use histogram oriented gradient and support vector machine for training, building human descriptor in detection, and finding possible human in the films. Our tracking method uses particle filtering technique. We approach from particle sampling and then select color distribution as target feature. We find weights by computing Bhattacharyya coefficient between the target and candidate particles, and use the weighted average to estimate the final target location. We improve particle filter method by adding edge feature to overcome the shortcomings of using only one single color feature and achieve better tracking accuracy. The tracking error is compared by RMSE (root mean squared error). If only the color feature is considered, the tracking error is about 74.18. With the help of edge feature the error is reduced to approximately 61.84. Experimental results verify that the proposed system has higher tracking accuracy and is more robust.

摘 要 i
致 謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1研究背景 1
1.2研究目的 2
1.3研究方法 2
1.4論文架構 3
第二章 相關技術及運用探討 4
2.1 人體偵測 4
2.1.1 物體的特徵 4
2.1.2 偵測常用的方法 6
2.1.3 旋轉梯度特徵(Histogram of Oriented Gradient) 8
2.1.4 支持向量機 11
2.2 人體追蹤 16
2.2.1 剪影追蹤(Silhouette tracking) 16
2.2.2 核心追蹤(Kernel tracking) 17
2.2.3 點追蹤(Point tracking) 19
第三章 系統架構與設計 26
3.1 系統架構 26
3.1.1 硬體架構 26
3.1.2 軟體架構 28
3.2 偵測階段 28
3.3 LIBSVM 36
3.4 追蹤階段 37
3.5 開發平台 45
第四章 實驗結果與分析 46
4.1 實驗架構 46
4.2 人體偵測結果 46
4.3 人體追蹤結果 53
4.4 實驗分析結果 66
第五章 結論與未來展望 70
5.1 結論 70
5.2 未來展望 71
參考文獻 72

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